def __call__(self, domain, field):
# Setup plan for calculating fast Fourier transforms:
self.plan = Plan(domain.total_samples, queue=self.queue)
field_temp = np.empty_like(field)
field_interaction = np.empty_like(field)
from pyofss.modules.linearity import Linearity
dispersion = Linearity(beta=[0.0, 0.0, 0.0, 1.0], sim_type="default")
factor = dispersion(domain)
self.send_arrays_to_device(
field, field_temp, field_interaction, factor)
stepsize = self.length / self.total_steps
zs = np.linspace(0.0, self.length, self.total_steps + 1)
#start = time.clock()
for z in zs[:-1]:
self.cl_rk4ip(self.buf_field, self.buf_temp,
self.buf_interaction, self.buf_factor, stepsize)
#stop = time.clock()
#cl_result = self.buf_field.get()
#print("cl_result: %e" % ((stop - start) / 1000.0))
return self.buf_field.get()
def __init__(self, in_scale, in_matrix, queue):
self.scale = tuple(in_scale)
# create plan
self.plan = Plan(self.scale, queue=queue)
# prepare data
self.data = in_matrix
self.gpu_data = cl_array.to_device(queue, self.data)
def __init__(self, total_samples, dorf, length=None, total_steps=None,
name="ocl_fibre"):
self.name = name
self.queue = None
self.np_float = None
self.np_complex = None
self.prg = None
self.cl_initialise(dorf)
self.plan = Plan(total_samples, queue=self.queue)
self.buf_field = None
self.buf_temp = None
self.buf_interaction = None
self.buf_factor = None
self.shape = None
self.plan = None
self.cached_factor = False
# Force usage of cached version of function:
self.cl_linear = self.cl_linear_cached
self.length = length
self.total_steps = total_steps
def gs_mod_gpu(idata, itera=10, osize=256):
cut = osize // 2
pl = cl.get_platforms()[0]
devices = pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,
dtype=complex128) #no funciona con "complex128"
src = str(
Template(KERNEL).render(
double_support=all(has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)))
prg = cl.Program(ctx, src).build()
idata_gpu = cl_array.to_device(queue,
ifftshift(idata).astype("complex128"))
fdata_gpu = cl_array.empty_like(idata_gpu)
rdata_gpu = cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data, fdata_gpu.data)
mask = exp(2.j * pi * random(idata.shape))
mask[512 - cut:512 + cut, 512 - cut:512 + cut] = 0
idata_gpu = cl_array.to_device(
queue,
ifftshift(idata + mask).astype("complex128"))
fdata_gpu = cl_array.empty_like(idata_gpu)
rdata_gpu = cl_array.empty_like(idata_gpu)
error_gpu = cl_array.to_device(ctx, queue,
zeros(idata_gpu.shape).astype("double"))
plan.execute(idata_gpu.data, fdata_gpu.data)
e = 1000
ea = 1000
for i in range(itera):
prg.norm(queue, fdata_gpu.shape, None, fdata_gpu.data)
plan.execute(fdata_gpu.data, rdata_gpu.data, inverse=True)
#~ prg.norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
norm1 = prg.norm1
norm1.set_scalar_arg_dtypes([None, None, None, int32])
norm1(queue, rdata_gpu.shape, None, rdata_gpu.data, idata_gpu.data,
error_gpu.data, int32(cut))
e = sqrt(cl_array.sum(error_gpu).get()) / (2 * cut)
#~ if e>ea:
#~
#~ break
#~ ea=e
plan.execute(rdata_gpu.data, fdata_gpu.data)
fdata = fdata_gpu.get()
fdata = ifftshift(fdata)
fdata = exp(1.j * angle(fdata))
return fdata
def _ocl_fft_gpu_inplace(ocl_arr,inverse = False, plan = None):
assert_bufs_type(np.complex64,ocl_arr)
if plan is None:
plan = Plan(ocl_arr.shape, queue = get_device().queue)
plan.execute(ocl_arr.data,ocl_arr.data, inverse = inverse)
def _ocl_fft_gpu_inplace(ocl_arr, inverse=False, plan=None):
assert_bufs_type(np.complex64, ocl_arr)
if plan is None:
plan = Plan(ocl_arr.shape, queue=get_device().queue)
plan.execute(ocl_arr.data, ocl_arr.data, inverse=inverse)
def __init__(self, in_size, kernel_size, batch_size, context, queue):
self.sizes = []
for i in xrange(len(in_size)):
self.sizes.append(get_power_of_two(in_size[i] + kernel_size[i] +
1))
self.sizes = tuple(self.sizes)
self.ctx = context
self.queue = queue
self.plan = Plan(self.sizes, queue=self.queue)
self.in_array = cl.array.zeros(
self.queue,
(batch_size, self.sizes[0], self.sizes[1], self.sizes[2]),
numpy.complex64)
self.kernel = cl.array.zeros(
self.queue,
(batch_size, self.sizes[0], self.sizes[1], self.sizes[2]),
numpy.complex64)
self.result_buffer = numpy.zeros(self.in_array.shape, numpy.complex64)
self.kernel_center = []
for i in xrange(len(kernel_size)):
self.kernel_center.append(kernel_size[i] / 2)
self.kernel_center = tuple(self.kernel_center)
self.halves = []
for i in xrange(len(kernel_size)):
self.halves.append(numpy.ceil(kernel_size[i] / 2.0))
self.halves = tuple(self.halves)
self.padding_locations = []
for i in xrange(len(self.sizes)):
# without this if even kernels result in an incorrect edge in the result
if kernel_size[i] % 2 == 0:
self.padding_locations.append(
-1 * ((in_size[i] - self.sizes[i]) / 2))
self.padding_locations.append(
-1 * ((self.sizes[i] - in_size[i]) / 2))
else:
self.padding_locations.append((self.sizes[i] - in_size[i]) / 2)
self.padding_locations.append((in_size[i] - self.sizes[i]) / 2)
self.padding_locations = tuple(self.padding_locations)
self.valid_locations = []
for i in xrange(len(self.sizes)):
self.valid_locations.append(self.padding_locations[(i * 2)] +
self.halves[i] - 1)
self.valid_locations.append(self.padding_locations[(i * 2)] +
self.halves[i] + in_size[i] -
kernel_size[i])
self.valid_locations = tuple(self.valid_locations)
self.full_locations = []
for i in xrange(len(self.sizes)):
offset = self.sizes[i] - (in_size[i] + kernel_size[i] - 1)
self.full_locations.append(offset / 2)
self.full_locations.append(-offset / 2)
self.batch_size = batch_size
def clifftn(data):
clear_first_arg_caches()
ctx = cl.create_some_context(interactive=False)
queue = cl.CommandQueue(ctx)
plan = Plan(data.shape, normalize=True, queue=queue)
# Inverse transform:
plan.execute(gpu_data.data, inverse=True)
result = gpu_data.get()
return result
def gs_mod_gpu(idata,itera=10,osize=256):
cut=osize//2
pl=cl.get_platforms()[0]
devices=pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,dtype=complex128) #no funciona con "complex128"
src = str(Template(KERNEL).render(
double_support=all(
has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)
))
prg = cl.Program(ctx,src).build()
idata_gpu=cl_array.to_device(queue, ifftshift(idata).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data,fdata_gpu.data)
mask=exp(2.j*pi*random(idata.shape))
mask[512-cut:512+cut,512-cut:512+cut]=0
idata_gpu=cl_array.to_device(queue, ifftshift(idata+mask).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
error_gpu=cl_array.to_device(ctx, queue, zeros(idata_gpu.shape).astype("double"))
plan.execute(idata_gpu.data,fdata_gpu.data)
e=1000
ea=1000
for i in range (itera):
prg.norm(queue, fdata_gpu.shape, None,fdata_gpu.data)
plan.execute(fdata_gpu.data,rdata_gpu.data,inverse=True)
#~ prg.norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
norm1=prg.norm1
norm1.set_scalar_arg_dtypes([None, None, None, int32])
norm1(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data,error_gpu.data, int32(cut))
e= sqrt(cl_array.sum(error_gpu).get())/(2*cut)
#~ if e>ea:
#~
#~ break
#~ ea=e
plan.execute(rdata_gpu.data,fdata_gpu.data)
fdata=fdata_gpu.get()
fdata=ifftshift(fdata)
fdata=exp(1.j*angle(fdata))
return fdata
def _ocl_fft_gpu(ocl_arr,res_arr = None,inverse = False, plan = None):
assert_bufs_type(np.complex64,ocl_arr)
if plan is None:
plan = Plan(ocl_arr.shape, queue = get_device().queue)
if res_arr is None:
res_arr = OCLArray.empty(ocl_arr.shape,np.complex64)
plan.execute(ocl_arr.data,res_arr.data, inverse = inverse)
return res_arr
def _ocl_fft_gpu(ocl_arr, res_arr=None, inverse=False, plan=None):
assert_bufs_type(np.complex64, ocl_arr)
if plan is None:
plan = Plan(ocl_arr.shape, queue=get_device().queue)
if res_arr is None:
res_arr = OCLArray.empty(ocl_arr.shape, np.complex64)
plan.execute(ocl_arr.data, res_arr.data, inverse=inverse)
return res_arr
def _prep_gpu():
""" Set up GPU calculation dependencies """
# try to import the necessary libraries
fallback = False
try:
import gpu
import string
import pyopencl as cl
import pyopencl.array as cla
from pyfft.cl import Plan
except ImportError:
fallback = True
# check gpu_info
try:
assert gpu.valid(gpu_info),\
"gpu_info in propagate_distances improperly specified"
context, device, queue, platform = gpu_info
except AssertionError:
fallback = True
if fallback:
propagate_distances(data, distances, energy_or_wavelength,
pixel_pitch, subregion=subregion,
silent=silent, band_limit=band_limit,
gpu_info=None, im_convert=im_convert)
# if everything is OK, allocate memory and build kernels
kp = string.join(gpu.__file__.split('/')[:-1], '/')+'/kernels/'
build = _build_helper(context, device, kp)
phase_multiply = build('propagate_phase_multiply.cl')
copy_to_buffer = build('propagate_copy_to_save_buffer.cl')
fftplan = Plan((N, N), queue=queue)
# put the signals onto the gpu along with buffers for the
# various operations
rarray = cla.to_device(queue, r.astype(np.float32))
fourier = cla.to_device(queue, data.astype(np.complex64))
phase = cla.empty(queue, (N, N), np.complex64)
back = cla.empty(queue, (N, N), np.complex64)
store = cla.empty(queue, (nf, rows, cols), np.complex64)
# precompute the fourier transform of data.
fftplan.execute(fourier.data, wait_for_finish=True)
return phase_multiply, copy_to_buffer, fftplan, rarray, fourier,\
phase, back, store, build
def clfftn(data):
""" OpenCL FFT 3D
"""
clear_first_arg_caches()
#ctx = cl.create_some_context(interactive=False)
#queue = cl.CommandQueue(ctx)
ctx, queue = clinit()
plan = Plan(data.shape, normalize=True, queue=queue)
# forward transform on device
gpu_data = cl_array.to_device(queue, data)
# forward transform
plan.execute(gpu_data.data)
#result = gpu_data.get()
result = gpu_data.get()
return result
def test_fft(self):
data = gpu_util.get_array(np.random.normal(100, 100,
size=(4, 4)).astype(cfg.PRECISION.np_float))
orig = gpu_util.get_host(data)
data = ip.fft_2(data)
ip.ifft_2(data)
np.testing.assert_almost_equal(orig, data.get().real, decimal=4)
# With a plan
from pyfft.cl import Plan
plan = Plan((4, 4), queue=cfg.OPENCL.queue)
data = ip.fft_2(np.copy(orig), plan=plan)
ip.ifft_2(data, plan=plan)
np.testing.assert_almost_equal(orig, data.get().real, decimal=4)
# Test double precision
syris.init(double_precision=True, device_index=0)
data = gpu_util.get_array(np.random.normal(100, 100,
size=(4, 4)).astype(cfg.PRECISION.np_float))
gt = np.fft.fft2(data.get())
data = ip.fft_2(data)
np.testing.assert_almost_equal(gt, data.get(), decimal=4)
gt = np.fft.ifft2(data.get())
data = ip.ifft_2(data)
np.testing.assert_almost_equal(gt, data.get(), decimal=4)
def _get_plan(itype,otype,inlen):
try:
theplan = _plans[(itype,otype,inlen)]
except KeyError:
theplan = Plan(inlen,dtype=itype,queue=pycbc.scheme.mgr.state.queue,normalize=False,fast_math=True)
_plans.update({(itype,otype,inlen) : theplan })
return theplan
def __init__(self, field):
self.ocl = OpenCLSettings.Instance()
self.dispIndex = DispersionIndex.Instance()
self.grid = Grid.Instance()
self.physConst = PhysicalEnvironment.Instance()
self.ocl = OpenCLSettings.Instance()
self.errors = ComputationalErrors()
self.need_update_energy = False
self.energy = numpy.empty(1).astype(numpy.float64)
self.layer = numpy.int32(0)
self.Z = numpy.int32(0)
self.z_limit = Settings.z
self.dz = Settings.dz
self.current_dz = numpy.float64()
self.calculated_dz = Settings.dz
if Settings.z_strategy == "Uniform":
self.z_step_strategy = UniformZStepStrategy()
else:
self.z_step_strategy = AdaptiveZStepStrategy()
self.global_iteration_number = numpy.int32(0)
self.unlinear_iterations = numpy.zeros(self.grid.space_size, dtype=numpy.int32)
self.E_next = numpy.zeros((self.grid.space_size, self.grid.time_size), dtype=numpy.float64)
self.D = numpy.zeros(self.grid.time_size, dtype=numpy.float64)
self.K = numpy.zeros(self.grid.time_size, dtype=numpy.float64)
self.field_shape = field.shape
self.test_field = numpy.zeros((self.grid.space_size, self.grid.time_size), dtype=numpy.complex128)
self.mf = cl.mem_flags
self.A1_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.grid.space_grid.nbytes)
self.A2_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.grid.space_grid.nbytes)
self.A3_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.grid.space_grid.nbytes)
self.D_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.D.nbytes)
self.K_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.K.nbytes)
self.space_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=self.grid.space_grid)
self.space_delta_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=self.grid.space_delta)
self.plan1D = Plan(self.grid.time_size, dtype=numpy.complex128, queue=self.ocl.queue)
self.field_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=field)
self.unlinear_iterations_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.unlinear_iterations.nbytes)
self.local_buf = cl.LocalMemory(8*self.ocl.n_threads)
self.buffers, self.sizes = self.__compute_partial_buffers(self.grid.space_size * self.grid.time_size,
self.ocl.n_threads)
self.nlnr_computing_time = numpy.float64(0)
self.diff_computing_time = numpy.float64(0)
self.disp_computing_time = numpy.float64(0)
self.copy_computing_time = numpy.float64(0)
self.energy_computing_time = numpy.float64(0)
def _ocl_fft_numpy(arr, inverse=False, plan=None):
if plan is None:
plan = Plan(arr.shape, queue=get_device().queue)
if arr.dtype != np.complex64:
logger.info("converting %s to complex64, might slow things down..." %
arr.dtype)
ocl_arr = OCLArray.from_array(arr.astype(np.complex64, copy=False))
_ocl_fft_gpu_inplace(ocl_arr, inverse=inverse, plan=plan)
return ocl_arr.get()
def kspacegaussian_filter_pyfftCL(ksp, sigma):
clear_first_arg_caches()
sz = ksp.shape
dtype = np.complex64
ftype = np.float32
#api = cluda.ocl_api()
ctx = cl.create_some_context(interactive=False)
queue = cl.CommandQueue(ctx)
queue.flush()
data_dev = cl_array.to_device(queue, ksp)
w = h = k = 512
plan = Plan((w, h, k), normalize=True, queue=queue)
FACTOR = 1.0
program = cl.Program(ctx, """
#pragma OPENCL EXTENSION cl_khr_fp64: enable
#include "pyopencl-complex.h"
__kernel void gauss_kernel(__global cfloat_t *dest) //, __global cfloat_t *src)
{
uint x = get_global_id(0);uint y = get_global_id(1);uint z = get_global_id(2);
uint dim1= %d;
uint dim2= %d;
uint dim3= %d;
float sigma[3];
sigma[0]=%f;sigma[1]=%f;sigma[2]=%f;
float factor = %f;
float TWOPISQ = 19.739208802178716; //6.283185307179586; //2*3.141592;
ulong idx = z*dim1*dim2 + y*dim1 + x;
float i = (float)(x); //(x / dim3) / dim2);
i = (i - (float)floor((float)(dim1)/2.0f))/(float)(dim1);
float j = (float)y; //(x / dim3);
//if((int)j > dim2) {j=(float)fmod(j, (float)dim2);};
j = (j - (float)floor((float)(dim2)/2.0f))/(float)(dim2);
//Account for large global index (stored as ulong) before performing modulus
//double pre_k=fmod((double)(x) , (double) dim3);
float k = (float) z; // pre_k;
k = (k - (float)floor((float)(dim3)/2.0f))/(float)(dim3);
float weight = exp(-TWOPISQ*((i*i)*sigma[0]*sigma[0] + (j*j)*sigma[1]*sigma[1] + (k*k)*sigma[2]*sigma[2]));
dest[idx].x = dest[idx].x * weight;
dest[idx].y = dest[idx].y * weight;
}
""" % (sz[0], sz[1], sz[2], sigma[0], sigma[1], sigma[2], FACTOR)).build()
gauss_kernel = program.gauss_kernel
#data_dev = thr.empty_like(ksp_dev)
gauss_kernel(queue, sz, None, data_dev.data).wait() # , data_dev.data
ksp_out = data_dev.get()
queue.flush()
ctx = cl.create_some_context(interactive=False)
queue = cl.CommandQueue(ctx)
w = h = k = 512
plan = Plan((w, h, k), normalize=True, queue=queue)
data2_dev = cl_array.to_device(queue, ksp_out)
plan.execute(data2_dev.data, inverse=True)
result = data2_dev.get()
result = np.fft.fftshift(result)
queue.finish()
return result # ,ksp_out
def __init__(self, insize, kernelsize, batchsize, context, queue):
self.sizes = []
for i in xrange(len(insize)):
self.sizes.append(getPowerOfTwo(insize[i] + kernelsize[i] + 1))
self.sizes = tuple(self.sizes)
self.ctx = context
self.queue = queue
self.plan = Plan(self.sizes, queue=self.queue)
self.inarray = cl.array.zeros(
self.queue, (batchsize, self.sizes[0], self.sizes[1], self.sizes[2]), numpy.complex64
)
self.kernel = cl.array.zeros(
self.queue, (batchsize, self.sizes[0], self.sizes[1], self.sizes[2]), numpy.complex64
)
self.result_buffer = numpy.zeros(self.inarray.shape, numpy.complex64)
self.kernel_center = []
for i in xrange(len(kernelsize)):
self.kernel_center.append(kernelsize[i] / 2)
self.kernel_center = tuple(self.kernel_center)
self.halves = []
for i in xrange(len(kernelsize)):
self.halves.append(numpy.ceil(kernelsize[i] / 2.0))
self.halves = tuple(self.halves)
self.padding_locations = []
for i in xrange(len(self.sizes)):
# without this if even kernels result in an incorrect edge in the result
if kernelsize[i] % 2 == 0:
self.padding_locations.append(-1 * ((insize[i] - self.sizes[i]) / 2))
self.padding_locations.append(-1 * ((self.sizes[i] - insize[i]) / 2))
else:
self.padding_locations.append((self.sizes[i] - insize[i]) / 2)
self.padding_locations.append((insize[i] - self.sizes[i]) / 2)
self.padding_locations = tuple(self.padding_locations)
self.valid_locations = []
for i in xrange(len(self.sizes)):
self.valid_locations.append(self.padding_locations[(i * 2)] + self.halves[i] - 1)
self.valid_locations.append(self.padding_locations[(i * 2)] + self.halves[i] + insize[i] - kernelsize[i])
self.valid_locations = tuple(self.valid_locations)
self.full_locations = []
for i in xrange(len(self.sizes)):
offset = self.sizes[i] - (insize[i] + kernelsize[i] - 1)
self.full_locations.append(offset / 2)
self.full_locations.append(-offset / 2)
self.batch_size = batchsize
class FastFourierTransform:
def __init__(self, in_scale, in_matrix, queue):
self.scale = tuple(in_scale)
# create plan
self.plan = Plan(self.scale, queue=queue)
# prepare data
self.data = in_matrix
self.gpu_data = cl_array.to_device(queue, self.data)
def fourier_transform(self):
# forward transform
self.plan.execute(self.gpu_data.data)
# print self.gpu_data.get()
# inverse transform
self.plan.execute(self.gpu_data.data, inverse=True)
result_ = self.gpu_data.get()
print(result_)
error = numpy.abs(
numpy.sum(numpy.abs(self.data) - numpy.abs(result_)) /
self.data.size)
if error > 1e-6:
s = 'error occurs'
raise ValueError('invalid value: %s' % s)
return result_
def python_ifft_guardinterval_func(self, ofdmmode, guardinterval, inputdata):
# create a fft plan
self.fftplan = Plan(ofdmmode,dtype=numpy.complex64,normalize=True, fast_math=True,context=self.ctx, queue=self.queue)
# opencl buffer holding data for the ifft - including pilots # 8k or 2k size ???
fftbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdmmode*8)
#size of guiardinterval destination buffer
self.dest_buf_size = ofdmmode*(1+guardinterval)
# opencl buffer holding the computed data
dest_buf = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=int(self.dest_buf_size*8) )
self.ret_buf = numpy.array(numpy.zeros(self.dest_buf_size), dtype=numpy.complex64)
cl.enqueue_copy(self.queue, fftbuffer, inputdata).wait()
self.fftplan.execute(fftbuffer, data_out=None, inverse=True, batch=1, wait_for_finish=True)
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(8*ofdmmode), src_offset=0, dest_offset=int(ofdmmode*guardinterval)).wait()
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(ofdmmode*guardinterval), src_offset=int(ofdmmode - ofdmmode*guardinterval) ,dest_offset=0).wait()
cl.enqueue_copy(self.queue, self.ret_buf, dest_buf).wait()
return self.ret_buf
def _fft_2(data, inverse=False, plan=None, queue=None, block=True):
"""Execute FFT on *data*, which is first converted to a pyopencl array and retyped to
complex.
"""
data = g_util.get_array(data, queue=queue)
if data.dtype != cfg.PRECISION.np_cplx:
data = data.astype(cfg.PRECISION.np_cplx)
if not plan:
if not queue:
queue = cfg.OPENCL.queue
if queue not in cfg.OPENCL.fft_plans:
cfg.OPENCL.fft_plans[queue] = {}
if data.shape not in cfg.OPENCL.fft_plans[queue]:
LOG.debug('Creating FFT Plan for {} and shape {}'.format(queue, data.shape))
cfg.OPENCL.fft_plans[queue][data.shape] = Plan(data.shape,
dtype=cfg.PRECISION.np_cplx,
queue=queue)
plan = cfg.OPENCL.fft_plans[queue][data.shape]
LOG.debug('fft_2, shape: %s, inverse: %s', data.shape, inverse)
plan.execute(data.data, inverse=inverse, wait_for_finish=block)
return data
class file_creator():
def __init__(self):
# create a opencl context
try:
self.ctx = cl.create_some_context()
except ValueError:
print "error %s\nExiting." % (ValueError)
# create a opencl command queue
self.queue = cl.CommandQueue(self.ctx)
#self.cd = computedevice
# create a event
self.thread_event = threading.Event()
self.duration = 15
def python_ifft_guardinterval_func(self, ofdmmode, guardinterval, inputdata):
# create a fft plan
self.fftplan = Plan(ofdmmode,dtype=numpy.complex64,normalize=True, fast_math=True,context=self.ctx, queue=self.queue)
# opencl buffer holding data for the ifft - including pilots # 8k or 2k size ???
fftbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdmmode*8)
#size of guiardinterval destination buffer
self.dest_buf_size = ofdmmode*(1+guardinterval)
# opencl buffer holding the computed data
dest_buf = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=int(self.dest_buf_size*8) )
self.ret_buf = numpy.array(numpy.zeros(self.dest_buf_size), dtype=numpy.complex64)
cl.enqueue_copy(self.queue, fftbuffer, inputdata).wait()
self.fftplan.execute(fftbuffer, data_out=None, inverse=True, batch=1, wait_for_finish=True)
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(8*ofdmmode), src_offset=0, dest_offset=int(ofdmmode*guardinterval)).wait()
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(ofdmmode*guardinterval), src_offset=int(ofdmmode - ofdmmode*guardinterval) ,dest_offset=0).wait()
cl.enqueue_copy(self.queue, self.ret_buf, dest_buf).wait()
return self.ret_buf
def test_execution_time(self):
print "test_execution_time:"
print "TODO"
def test_stop(self):
self.thread_event.set()
def load_kernel(self, filename, kernelname):
print "Kernel \"%s\" from file \"%s\" :" % (kernelname,filename)
mf = cl.mem_flags
#read in the OpenCL source file as a string
self.f = open(filename, 'r')
fstr = "".join(self.f.readlines())
self.program = cl.Program(self.ctx, fstr)
self.program.build()
self.f.close()
#create the opencl kernel
return cl.Kernel(self.program,kernelname)
def test_algorithmA(self, ofdm_mode, guardinterval):
print "\n**************************"
print "test ofdm numpy ifft with fftshift"
passed = 0
linecnt = 1
g = 0
size = 0
if ofdm_mode == 8192:
size = 6817
print "8k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_8K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_8K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_8K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_8K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
elif ofdm_mode == 2048:
size = 1705
print "2k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_2K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_2K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_2K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_2K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
if g == 0:
print "wrong guardinterval specified"
return
if size == 0:
print "wrong ofdm_mode"
return
for line in self.fd_input:
data_to_encode = [float(0) + 1j*float(0)] * ofdm_mode
counter = (ofdm_mode-size-1)/2+1
#counter = 0
for tmp in line.split(","):
if string.find(tmp, " + ") > -1:
data_to_encode[counter]=(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
data_to_encode[counter]=(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
counter += 1
data_to_encode = numpy.array(data_to_encode, dtype=numpy.complex128)
reference_data = []
for tmp in self.fd_output.readline().split(","):
if string.find(tmp, " + ") > -1:
reference_data.append(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
reference_data.append(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
reference_data = numpy.array(reference_data, dtype=numpy.complex128)
encoded_data = numpy.fft.ifft(numpy.fft.fftshift(data_to_encode))
# add guard interval
tmp = []
for i in range(0,int(ofdm_mode*g)):
tmp.append(encoded_data[(ofdm_mode*(1-g))+i])
for i in range(0,ofdm_mode):
tmp.append(encoded_data[i])
encoded_data = tmp
if numpy.allclose(reference_data, encoded_data, rtol=1.0000000000000001e-04, atol=1e-06):
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
print data_to_encode
print "encoded data[0]:"
print encoded_data[0]
print "reference data[0]:"
print reference_data[0]
print "error data:"
print reference_data - encoded_data
linecnt += 1
print "%d pass out of %d" % (passed, linecnt-1)
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All ofdm ifft tests PASS\n"
return True
else:
print "at least one ofdm ifft test FAILED\n"
return False
def test_algorithmB(self, ofdm_mode, guardinterval):
print "\n**************************"
print "test ofdm numpy ifft w/o fftshift"
passed = 0
linecnt = 1
g = 0
size = 0
if ofdm_mode == 8192:
size = 6817
print "8k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_8K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_8K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_8K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_8K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
elif ofdm_mode == 2048:
size = 1705
print "2k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_2K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_2K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_2K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_2K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
if g == 0:
print "wrong guardinterval specified"
return
if size == 0:
print "wrong ofdm_mode"
return
for line in self.fd_input:
data_to_encode = [float(0) + 1j*float(0)] * ofdm_mode
#counter = (2048-size-1)/2+1
counter = 0
for tmp in line.split(","):
if string.find(tmp, " + ") > -1:
data_to_encode[counter]=(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
data_to_encode[counter]=(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
counter += 1
data_to_encode = numpy.array(data_to_encode, dtype=numpy.complex128)
reference_data = []
for tmp in self.fd_output.readline().split(","):
if string.find(tmp, " + ") > -1:
reference_data.append(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
reference_data.append(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
reference_data = numpy.array(reference_data, dtype=numpy.complex128)
# do fftshift
tmp = [float(0) + 1j*float(0)] * ofdm_mode
for i in range(0,size):
tmp[(ofdm_mode-size+1)/2+i] = data_to_encode[i]
for i in range(0,ofdm_mode/2):
data_to_encode[i] = tmp[ofdm_mode/2+i]
data_to_encode[i+ofdm_mode/2] = tmp[i]
encoded_data = numpy.fft.ifft(data_to_encode)
# add guard interval
tmp = []
for i in range(0,int(ofdm_mode*g)):
tmp.append(encoded_data[(ofdm_mode*(1-g))+i])
for i in range(0,ofdm_mode):
tmp.append(encoded_data[i])
encoded_data = tmp
if numpy.allclose(reference_data, encoded_data, rtol=1.0000000000000001e-04, atol=1e-06):
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
#print data_to_encode
print "encoded data[0]:"
print encoded_data[0]
print "reference data[0]:"
print reference_data[0]
print "error data:"
#print reference_data - encoded_data
linecnt += 1
print "%d pass out of %d" % (passed, linecnt-1)
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All ofdm ifft tests PASS\n"
return True
else:
print "at least one ofdm ifft test FAILED\n"
return False
def test_algorithmC(self, ofdm_mode, guardinterval):
print "\n**************************"
print "test ofdm opencl ifft w/o fftshift"
passed = 0
linecnt = 1
g = 0
size = 0
# create a fft plan
self.fftplan = Plan(ofdm_mode,dtype=numpy.complex128,normalize=True, fast_math=True,context=self.ctx, queue=self.queue)
# opencl buffer holding data for the ifft - including pilots # 8k or 2k size ???
fftbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdm_mode*16)
#size of guiardinterval destination buffer
dest_buf_size = ofdm_mode*(1+guardinterval)
# opencl buffer holding the computed data
dest_buf = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=int(dest_buf_size*16) )
encoded_data = numpy.array(numpy.zeros(dest_buf_size), dtype=numpy.complex128)
if ofdm_mode == 8192:
size = 6817
print "8k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_8K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_8K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_8K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_8K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
elif ofdm_mode == 2048:
size = 1705
print "2k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_2K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_2K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_2K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_2K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
if g == 0:
print "wrong guardinterval specified"
return
if size == 0:
print "wrong ofdm_mode"
return
for line in self.fd_input:
data_to_encode = [float(0) + 1j*float(0)] * ofdm_mode
#counter = (2048-size-1)/2+1
counter = 0
for tmp in line.split(","):
if string.find(tmp, " + ") > -1:
data_to_encode[counter]=(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
data_to_encode[counter]=(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
counter += 1
data_to_encode = numpy.array(data_to_encode, dtype=numpy.complex128)
reference_data = []
for tmp in self.fd_output.readline().split(","):
if string.find(tmp, " + ") > -1:
reference_data.append(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
reference_data.append(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
reference_data = numpy.array(reference_data, dtype=numpy.complex128)
# do fftshift
tmp = [float(0) + 1j*float(0)] * ofdm_mode
for i in range(0,size):
tmp[(ofdm_mode-size+1)/2+i] = data_to_encode[i]
for i in range(0,ofdm_mode/2):
data_to_encode[i] = tmp[ofdm_mode/2+i]
data_to_encode[i+ofdm_mode/2] = tmp[i]
cl.enqueue_copy(self.queue, fftbuffer, data_to_encode).wait()
self.fftplan.execute(fftbuffer, data_out=None, inverse=True, batch=1, wait_for_finish=True)
# add guard interval
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(16*ofdm_mode), src_offset=0, dest_offset=int(ofdm_mode*guardinterval*16)).wait()
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(ofdm_mode*guardinterval*16), src_offset=int(ofdm_mode - ofdm_mode*guardinterval)*16 ,dest_offset=0).wait()
cl.enqueue_copy(self.queue, encoded_data, dest_buf).wait()
if numpy.allclose(reference_data, encoded_data, rtol=1.0000000000000001e-04, atol=1e-06):
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
#print data_to_encode
print "encoded data[0]:"
print encoded_data[0]
print "reference data[0]:"
print reference_data[0]
print "error data:"
#print reference_data - encoded_data
linecnt += 1
print "%d pass out of %d" % (passed, linecnt-1)
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All ofdm ifft tests PASS\n"
return True
else:
print "at least one ofdm ifft test FAILED\n"
return False
def test_algorithmD(self, ofdm_mode, guardinterval):
print "\n**************************"
print "test ofdm opencl ifft w/o fftshift http://ochafik.com/ dft"
passed = 0
linecnt = 1
g = 0
size = 0
# create a kernel
kernel = self.load_kernel("DiscreteFourierTransformProgram.cl", "dft")
#size of guiardinterval destination buffer
dest_buf_size = ofdm_mode*(1+guardinterval)
self.inputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdm_mode*16)
# opencl buffer
self.outputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdm_mode*16)
# opencl buffer holding the computed data
dest_buf = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=int(dest_buf_size*16) )
encoded_data = numpy.array(numpy.zeros(dest_buf_size), dtype=numpy.complex128)
if ofdm_mode == 8192:
size = 6817
print "8k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_8K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_8K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_8K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_8K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
elif ofdm_mode == 2048:
size = 1705
print "2k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_2K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_2K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_2K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_2K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
if g == 0:
print "wrong guardinterval specified"
return
if size == 0:
print "wrong ofdm_mode"
return
for line in self.fd_input:
data_to_encode = [float(0) + 1j*float(0)] * ofdm_mode
counter = 0
for tmp in line.split(","):
if string.find(tmp, " + ") > -1:
data_to_encode[counter]=(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
data_to_encode[counter]=(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
counter += 1
data_to_encode = numpy.array(data_to_encode, dtype=numpy.complex128)
reference_data = []
for tmp in self.fd_output.readline().split(","):
if string.find(tmp, " + ") > -1:
reference_data.append(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
reference_data.append(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
reference_data = numpy.array(reference_data, dtype=numpy.complex128)
# do fftshift
tmp = [float(0) + 1j*float(0)] * ofdm_mode
for i in range(0,size):
tmp[(ofdm_mode-size+1)/2+i] = data_to_encode[i]
for i in range(0,ofdm_mode/2):
data_to_encode[i] = tmp[ofdm_mode/2+i]
data_to_encode[i+ofdm_mode/2] = tmp[i]
cl.enqueue_copy(self.queue, self.inputbuffer, data_to_encode)
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(ofdm_mode),numpy.int32(-1))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode),),None).wait()
cl.enqueue_copy(self.queue, dest_buf, self.outputbuffer, byte_count=int(16*ofdm_mode), src_offset=0, dest_offset=int(ofdm_mode*guardinterval*16)).wait()
cl.enqueue_copy(self.queue, dest_buf, self.outputbuffer, byte_count=int(ofdm_mode*guardinterval*16), src_offset=int(ofdm_mode - ofdm_mode*guardinterval)*16 ,dest_offset=0).wait()
cl.enqueue_copy(self.queue, encoded_data, dest_buf)
if numpy.allclose(reference_data, encoded_data, rtol=1.0000000000000001e-04, atol=1e-06):
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
print data_to_encode
print "encoded data[0]:"
print encoded_data[0]
print "reference data[0]:"
print reference_data[0]
print "error data:"
#print reference_data - encoded_data
linecnt += 1
print "%d pass out of %d" % (passed, linecnt-1)
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All ofdm ifft tests PASS\n"
return True
else:
print "at least one ofdm ifft test FAILED\n"
return False
def test_algorithmE(self, ofdm_mode, guardinterval):
print "\n**************************"
print "test ofdm opencl ifft bealto.com radix 2 fft"
passed = 0
linecnt = 1
g = 0
size = 0
if ofdm_mode == 8192:
size = 6817
print "8k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_8K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_8K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_8K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_8K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
elif ofdm_mode == 2048:
size = 1705
print "2k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_2K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_2K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_2K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_2K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
if g == 0:
print "wrong guardinterval specified"
return
if size == 0:
print "wrong ofdm_mode"
return
kernel = self.load_kernel("../FFT.cl", "fftRadix2Kernel")
swapkernel = self.load_kernel("../FFT.cl", "fftswaprealimag")
#size of guiardinterval destination buffer
dest_buf_size = ofdm_mode*(1+guardinterval)
self.tmpbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=size*8)
self.inputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdm_mode*8)
# opencl buffer
self.outputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdm_mode*8)
# opencl buffer holding the computed data
dest_buf = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=int(dest_buf_size*8) )
encoded_data = numpy.array(numpy.zeros(dest_buf_size), dtype=numpy.complex64)
for line in self.fd_input:
data_to_encode = numpy.array([float(0) + 1j*float(0)] * size, dtype=numpy.complex64)
#data_to_encode = numpy.array([float(0) + 1j*float(0)] * ofdm_mode, dtype=numpy.complex64)
cl.enqueue_copy(self.queue, self.inputbuffer, data_to_encode)
cl.enqueue_copy(self.queue, self.outputbuffer, data_to_encode)
counter = 0
for tmp in line.split(","):
if string.find(tmp, " + ") > -1:
data_to_encode[counter]=(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
data_to_encode[counter]=(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
counter += 1
data_to_encode = numpy.array(data_to_encode, dtype=numpy.complex64)
reference_data = []
for tmp in self.fd_output.readline().split(","):
if string.find(tmp, " + ") > -1:
reference_data.append(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
reference_data.append(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
reference_data = numpy.array(reference_data, dtype=numpy.complex64)
# do fftshift
cl.enqueue_copy(self.queue, self.inputbuffer,numpy.array([float(0) + 1j*float(0)] * ofdm_mode, dtype=numpy.complex64))
cl.enqueue_copy(self.queue, self.tmpbuffer, data_to_encode)
cl.enqueue_copy(self.queue, self.inputbuffer, self.tmpbuffer, byte_count=((size-1)/2)*8,src_offset=0,dest_offset=(ofdm_mode-(size-1)/2)*8)
cl.enqueue_copy(self.queue, self.inputbuffer, self.tmpbuffer, byte_count=((size+1)/2)*8,src_offset=((size-1)/2)*8,dest_offset=0)
swapkernel.set_args(self.inputbuffer)
cl.enqueue_nd_range_kernel(self.queue,swapkernel,(int(ofdm_mode),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(1))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.outputbuffer, self.inputbuffer, numpy.int32(2))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(4))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.outputbuffer, self.inputbuffer, numpy.int32(8))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(16))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.outputbuffer, self.inputbuffer, numpy.int32(32))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(64))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.outputbuffer, self.inputbuffer, numpy.int32(128))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(256))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.outputbuffer, self.inputbuffer, numpy.int32(512))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(1024))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
if ofdm_mode == 8192:
kernel.set_args(self.outputbuffer, self.inputbuffer, numpy.int32(2048))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer, numpy.int32(4096))
cl.enqueue_nd_range_kernel(self.queue,kernel,(int(ofdm_mode/2),),None).wait()
swapkernel.set_args(self.outputbuffer)
cl.enqueue_nd_range_kernel(self.queue,swapkernel,(int(ofdm_mode),),None).wait()
cl.enqueue_copy(self.queue, dest_buf, self.outputbuffer, byte_count=int(8*ofdm_mode), src_offset=0, dest_offset=int(ofdm_mode*guardinterval*8)).wait()
cl.enqueue_copy(self.queue, dest_buf, self.outputbuffer, byte_count=int(ofdm_mode*guardinterval*8), src_offset=int(ofdm_mode - ofdm_mode*guardinterval)*8 ,dest_offset=0).wait()
cl.enqueue_copy(self.queue, encoded_data, dest_buf)
if numpy.allclose(reference_data, encoded_data, rtol=1.0000000000000001e-04, atol=1e-06):
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
print data_to_encode
print "encoded data[0]:"
print encoded_data[0]
print "reference data[0]:"
print reference_data[0]
print "error data:"
#print reference_data - encoded_data
linecnt += 1
print "%d pass out of %d" % (passed, linecnt-1)
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All ofdm ifft tests PASS\n"
return True
else:
print "at least one ofdm ifft test FAILED\n"
return False
class OpenclFibre(object):
"""
This optical module is similar to Fibre, but uses PyOpenCl (Python
bindings around OpenCL) to generate parallelised code.
"""
def __init__(self, total_samples, dorf, length=None, total_steps=None,
name="ocl_fibre"):
self.name = name
self.queue = None
self.np_float = None
self.np_complex = None
self.prg = None
self.cl_initialise(dorf)
self.plan = Plan(total_samples, queue=self.queue)
self.buf_field = None
self.buf_temp = None
self.buf_interaction = None
self.buf_factor = None
self.shape = None
self.plan = None
self.cached_factor = False
# Force usage of cached version of function:
self.cl_linear = self.cl_linear_cached
self.length = length
self.total_steps = total_steps
def __call__(self, domain, field):
# Setup plan for calculating fast Fourier transforms:
self.plan = Plan(domain.total_samples, queue=self.queue)
field_temp = np.empty_like(field)
field_interaction = np.empty_like(field)
from pyofss.modules.linearity import Linearity
dispersion = Linearity(beta=[0.0, 0.0, 0.0, 1.0], sim_type="default")
factor = dispersion(domain)
self.send_arrays_to_device(
field, field_temp, field_interaction, factor)
stepsize = self.length / self.total_steps
zs = np.linspace(0.0, self.length, self.total_steps + 1)
#start = time.clock()
for z in zs[:-1]:
self.cl_rk4ip(self.buf_field, self.buf_temp,
self.buf_interaction, self.buf_factor, stepsize)
#stop = time.clock()
#cl_result = self.buf_field.get()
#print("cl_result: %e" % ((stop - start) / 1000.0))
return self.buf_field.get()
def cl_initialise(self, dorf="float"):
""" Initialise opencl related parameters. """
float_conversions = {"float": np.float32, "double": np.float64}
complex_conversions = {"float": np.complex64, "double": np.complex128}
self.np_float = float_conversions[dorf]
self.np_complex = complex_conversions[dorf]
for platform in cl.get_platforms():
if platform.name == "NVIDIA CUDA":
print("Using compiler optimisations suitable for Nvidia GPUs")
compiler_options = "-cl-mad-enable -cl-fast-relaxed-math"
else:
compiler_options = ""
ctx = cl.create_some_context(interactive=False)
self.queue = cl.CommandQueue(ctx)
substitutions = {"dorf": dorf}
code = OPENCL_OPERATIONS.substitute(substitutions)
self.prg = cl.Program(ctx, code).build(options=compiler_options)
@staticmethod
def print_device_info():
""" Output information on each OpenCL platform and device. """
for platform in cl.get_platforms():
print "=" * 60
print "Platform information:"
print "Name: ", platform.name
print "Profile: ", platform.profile
print "Vender: ", platform.vendor
print "Version: ", platform.version
for device in platform.get_devices():
print "-" * 60
print "Device information:"
print "Name: ", device.name
print "Type: ", cl.device_type.to_string(device.type)
print "Memory: ", device.global_mem_size // (1024 ** 2), "MB"
print "Max clock speed: ", device.max_clock_frequency, "MHz"
print "Compute units: ", device.max_compute_units
print "=" * 60
def send_arrays_to_device(self, field, field_temp,
field_interaction, factor):
""" Move numpy arrays onto compute device. """
self.shape = field.shape
self.buf_field = cl_array.to_device(
self.queue, field.astype(self.np_complex))
self.buf_temp = cl_array.to_device(
self.queue, field_temp.astype(self.np_complex))
self.buf_interaction = cl_array.to_device(
self.queue, field_interaction.astype(self.np_complex))
self.buf_factor = cl_array.to_device(
self.queue, factor.astype(self.np_complex))
def cl_copy(self, first_buffer, second_buffer):
""" Copy contents of one buffer into another. """
self.prg.cl_copy(self.queue, self.shape, None,
first_buffer.data, second_buffer.data)
def cl_linear(self, field_buffer, stepsize, factor_buffer):
""" Linear part of step. """
self.plan.execute(field_buffer.data, inverse=True)
self.prg.cl_linear(self.queue, self.shape, None, field_buffer.data,
factor_buffer.data, self.np_float(stepsize))
self.plan.execute(field_buffer.data)
def cl_linear_cached(self, field_buffer, stepsize, factor_buffer):
""" Linear part of step (cached version). """
if (self.cached_factor is False):
print "Caching factor"
self.prg.cl_cache(self.queue, self.shape, None,
factor_buffer.data, self.np_float(stepsize))
self.cached_factor = True
self.plan.execute(field_buffer.data, inverse=True)
self.prg.cl_linear_cached(self.queue, self.shape, None,
field_buffer.data, factor_buffer.data)
self.plan.execute(field_buffer.data)
def cl_nonlinear(self, field_buffer, stepsize, gamma=100.0):
""" Nonlinear part of step. """
self.prg.cl_nonlinear(self.queue, self.shape, None, field_buffer.data,
self.np_float(gamma), self.np_float(stepsize))
def cl_sum(self, first_buffer, first_factor, second_buffer, second_factor):
""" Calculate weighted summation. """
self.prg.cl_sum(self.queue, self.shape, None,
first_buffer.data, self.np_float(first_factor),
second_buffer.data, self.np_float(second_factor))
def cl_rk4ip(self, field, field_temp, field_interaction, factor, stepsize):
""" Runge-Kutta in the interaction picture method using OpenCL. """
inv_six = 1.0 / 6.0
inv_three = 1.0 / 3.0
half_step = 0.5 * stepsize
self.cl_copy(field_temp, field)
self.cl_linear(field, half_step, factor)
self.cl_copy(field_interaction, field)
self.cl_nonlinear(field_temp, stepsize)
self.cl_linear(field_temp, half_step, factor)
self.cl_sum(field, 1.0, field_temp, inv_six)
self.cl_sum(field_temp, 0.5, field_interaction, 1.0)
self.cl_nonlinear(field_temp, stepsize)
self.cl_sum(field, 1.0, field_temp, inv_three)
self.cl_sum(field_temp, 0.5, field_interaction, 1.0)
self.cl_nonlinear(field_temp, stepsize)
self.cl_sum(field, 1.0, field_temp, inv_three)
self.cl_sum(field_temp, 1.0, field_interaction, 1.0)
self.cl_linear(field_interaction, half_step, factor)
self.cl_linear(field, half_step, factor)
self.cl_nonlinear(field_temp, stepsize)
self.cl_sum(field, 1.0, field_temp, inv_six)
imggauss = kspacegaussian_filter_CL(ksp, np.ones(3), ctx) print 'PyFFT +OpenCL Gaussian filter:' toc() tic() print 'Complex K-space filter + Numpy IFFT' kspgauss2 = KSP.kspacegaussian_filter2(ksp, 1) image_filtered = simpleifft(procpar, dims, hdr, kspgauss2, args) toc() # PYFFT tic() #ctx = cl.create_some_context(interactive=False) #queue = cl.CommandQueue(ctx) w = h = k = 512 plan = Plan((w, h, k), normalize=True, queue=queue) gpu_data = cl_array.to_device(queue, ksp) plan.execute(gpu_data.data, inverse=True) result = gpu_data.get() toc() result = np.fft.fftshift(result) print "PyFFT OpenCL IFFT time and first three results:" print "%s sec, %s" % (toc(), str(np.abs(result[:3, 0, 0]))) tic() reference = np.fft.fftshift(np.fft.ifftn(ksp)) print "Numpy IFFTN time and first three results:" print "%s sec, %s" % (toc(), str(np.abs(reference[:3, 0, 0]))) print "Calulating L1 norm " print np.linalg.norm(result - reference) / np.linalg.norm(reference)
def fft_plan(shape):
"""returns an opencl/pyfft plan of shape dshape"""
return Plan(shape, queue=get_device().queue)
class Convolve:
""" Class that computes the necessary information to perform a
convolution and provides the actual convolution function. Can handle
2d or 3d convolutions. """
def __init__(self, in_size, kernel_size, batch_size, context, queue):
self.sizes = []
for i in xrange(len(in_size)):
self.sizes.append(get_power_of_two(in_size[i] + kernel_size[i] +
1))
self.sizes = tuple(self.sizes)
self.ctx = context
self.queue = queue
self.plan = Plan(self.sizes, queue=self.queue)
self.in_array = cl.array.zeros(
self.queue,
(batch_size, self.sizes[0], self.sizes[1], self.sizes[2]),
numpy.complex64)
self.kernel = cl.array.zeros(
self.queue,
(batch_size, self.sizes[0], self.sizes[1], self.sizes[2]),
numpy.complex64)
self.result_buffer = numpy.zeros(self.in_array.shape, numpy.complex64)
self.kernel_center = []
for i in xrange(len(kernel_size)):
self.kernel_center.append(kernel_size[i] / 2)
self.kernel_center = tuple(self.kernel_center)
self.halves = []
for i in xrange(len(kernel_size)):
self.halves.append(numpy.ceil(kernel_size[i] / 2.0))
self.halves = tuple(self.halves)
self.padding_locations = []
for i in xrange(len(self.sizes)):
# without this if even kernels result in an incorrect edge in the result
if kernel_size[i] % 2 == 0:
self.padding_locations.append(
-1 * ((in_size[i] - self.sizes[i]) / 2))
self.padding_locations.append(
-1 * ((self.sizes[i] - in_size[i]) / 2))
else:
self.padding_locations.append((self.sizes[i] - in_size[i]) / 2)
self.padding_locations.append((in_size[i] - self.sizes[i]) / 2)
self.padding_locations = tuple(self.padding_locations)
self.valid_locations = []
for i in xrange(len(self.sizes)):
self.valid_locations.append(self.padding_locations[(i * 2)] +
self.halves[i] - 1)
self.valid_locations.append(self.padding_locations[(i * 2)] +
self.halves[i] + in_size[i] -
kernel_size[i])
self.valid_locations = tuple(self.valid_locations)
self.full_locations = []
for i in xrange(len(self.sizes)):
offset = self.sizes[i] - (in_size[i] + kernel_size[i] - 1)
self.full_locations.append(offset / 2)
self.full_locations.append(-offset / 2)
self.batch_size = batch_size
def convolution(self, input_matrix, kernel, type_='valid'):
in_array = numpy.zeros(
(self.batch_size, self.sizes[0], self.sizes[1], self.sizes[2]),
numpy.complex64)
in_array[:, self.padding_locations[0]:self.padding_locations[1],
self.padding_locations[2]:self.padding_locations[3], self.
padding_locations[4]:self.padding_locations[5]] = input_matrix
self.in_array = cl.array.to_device(self.queue, in_array)
kernel_buffer = numpy.zeros(
(self.batch_size, self.sizes[0], self.sizes[1], self.sizes[2]),
numpy.complex64)
kernel_buffer[:, :self.halves[0], :self.halves[1], :self.halves[2]] = \
kernel[self.kernel_center[0]:, self.kernel_center[1]:, self.kernel_center[2]:]
kernel_buffer[:, :self.halves[0], :self.halves[1], -self.kernel_center[2]:] = \
kernel[self.kernel_center[0]:, self.kernel_center[1]:, :self.kernel_center[2]]
kernel_buffer[:, :self.halves[0], -self.kernel_center[1]:, :self.halves[2]] = \
kernel[self.kernel_center[0]:, :self.kernel_center[1], self.kernel_center[2]:]
kernel_buffer[:, :self.halves[0], -self.kernel_center[1]:, -self.kernel_center[2]:] = \
kernel[self.kernel_center[0]:, :self.kernel_center[1], :self.kernel_center[2]]
if kernel.shape[0] > 1:
kernel_buffer[:, -self.kernel_center[0]:, :self.halves[1], :self.halves[2]] = \
kernel[:self.kernel_center[0], self.kernel_center[1]:, self.kernel_center[2]:]
kernel_buffer[:, -self.kernel_center[0]:, :self.halves[1], -self.kernel_center[2]:] = \
kernel[:self.kernel_center[0], self.kernel_center[1]:, :self.kernel_center[2]]
kernel_buffer[:, -self.kernel_center[0]:, -self.kernel_center[1]:, :self.halves[2]] = \
kernel[:self.kernel_center[0], :self.kernel_center[1], self.kernel_center[2]:]
kernel_buffer[:, -self.kernel_center[0]:, -self.kernel_center[1]:, -self.kernel_center[2]:] = \
kernel[:self.kernel_center[0], :self.kernel_center[1], :self.kernel_center[2]]
self.kernel = cl.array.to_device(self.queue, kernel_buffer)
# fourier transform, point wise multiply, then invert => convolution
self.plan.execute(self.in_array.data, batch=self.batch_size)
self.plan.execute(self.kernel.data, batch=self.batch_size)
self.result_buffer = self.in_array * self.kernel
self.plan.execute(self.result_buffer.data,
inverse=True,
batch=self.batch_size)
result = self.result_buffer.get().astype(float)
if type_ == 'same':
return result[:,
self.padding_locations[0]:self.padding_locations[1],
self.padding_locations[2]:self.padding_locations[3],
self.padding_locations[4]:self.padding_locations[5]]
elif type_ == 'full':
return result[:, self.full_locations[0]:self.full_locations[1],
self.full_locations[2]:self.full_locations[3],
self.full_locations[4]:self.full_locations[5]]
elif type_ == 'valid':
return result[:, self.valid_locations[0]:self.valid_locations[1],
self.valid_locations[2]:self.valid_locations[3],
self.valid_locations[4]:self.valid_locations[5]]
class Convolver:
""" Class that computes the necessary information to perform a
convolution and provides the actual convolution function. Can handle
2d or 3d convolutions. """
def __init__(self, insize, kernelsize, batchsize, context, queue):
self.sizes = []
for i in xrange(len(insize)):
self.sizes.append(getPowerOfTwo(insize[i] + kernelsize[i] + 1))
self.sizes = tuple(self.sizes)
self.ctx = context
self.queue = queue
self.plan = Plan(self.sizes, queue=self.queue)
self.inarray = cl.array.zeros(
self.queue, (batchsize, self.sizes[0], self.sizes[1], self.sizes[2]), numpy.complex64
)
self.kernel = cl.array.zeros(
self.queue, (batchsize, self.sizes[0], self.sizes[1], self.sizes[2]), numpy.complex64
)
self.result_buffer = numpy.zeros(self.inarray.shape, numpy.complex64)
self.kernel_center = []
for i in xrange(len(kernelsize)):
self.kernel_center.append(kernelsize[i] / 2)
self.kernel_center = tuple(self.kernel_center)
self.halves = []
for i in xrange(len(kernelsize)):
self.halves.append(numpy.ceil(kernelsize[i] / 2.0))
self.halves = tuple(self.halves)
self.padding_locations = []
for i in xrange(len(self.sizes)):
# without this if even kernels result in an incorrect edge in the result
if kernelsize[i] % 2 == 0:
self.padding_locations.append(-1 * ((insize[i] - self.sizes[i]) / 2))
self.padding_locations.append(-1 * ((self.sizes[i] - insize[i]) / 2))
else:
self.padding_locations.append((self.sizes[i] - insize[i]) / 2)
self.padding_locations.append((insize[i] - self.sizes[i]) / 2)
self.padding_locations = tuple(self.padding_locations)
self.valid_locations = []
for i in xrange(len(self.sizes)):
self.valid_locations.append(self.padding_locations[(i * 2)] + self.halves[i] - 1)
self.valid_locations.append(self.padding_locations[(i * 2)] + self.halves[i] + insize[i] - kernelsize[i])
self.valid_locations = tuple(self.valid_locations)
self.full_locations = []
for i in xrange(len(self.sizes)):
offset = self.sizes[i] - (insize[i] + kernelsize[i] - 1)
self.full_locations.append(offset / 2)
self.full_locations.append(-offset / 2)
self.batch_size = batchsize
def convolution(self, A, kernel, type="valid"):
inarray = numpy.zeros((self.batch_size, self.sizes[0], self.sizes[1], self.sizes[2]), numpy.complex64)
inarray[
:,
self.padding_locations[0] : self.padding_locations[1],
self.padding_locations[2] : self.padding_locations[3],
self.padding_locations[4] : self.padding_locations[5],
] = A
self.inarray = cl.array.to_device(self.queue, inarray)
kernel_buffer = numpy.zeros((self.batch_size, self.sizes[0], self.sizes[1], self.sizes[2]), numpy.complex64)
kernel_buffer[:, : self.halves[0], : self.halves[1], : self.halves[2]] = kernel[
self.kernel_center[0] :, self.kernel_center[1] :, self.kernel_center[2] :
]
kernel_buffer[:, : self.halves[0], : self.halves[1], -self.kernel_center[2] :] = kernel[
self.kernel_center[0] :, self.kernel_center[1] :, : self.kernel_center[2]
]
kernel_buffer[:, : self.halves[0], -self.kernel_center[1] :, : self.halves[2]] = kernel[
self.kernel_center[0] :, : self.kernel_center[1], self.kernel_center[2] :
]
kernel_buffer[:, : self.halves[0], -self.kernel_center[1] :, -self.kernel_center[2] :] = kernel[
self.kernel_center[0] :, : self.kernel_center[1], : self.kernel_center[2]
]
if kernel.shape[0] > 1:
kernel_buffer[:, -self.kernel_center[0] :, : self.halves[1], : self.halves[2]] = kernel[
: self.kernel_center[0], self.kernel_center[1] :, self.kernel_center[2] :
]
kernel_buffer[:, -self.kernel_center[0] :, : self.halves[1], -self.kernel_center[2] :] = kernel[
: self.kernel_center[0], self.kernel_center[1] :, : self.kernel_center[2]
]
kernel_buffer[:, -self.kernel_center[0] :, -self.kernel_center[1] :, : self.halves[2]] = kernel[
: self.kernel_center[0], : self.kernel_center[1], self.kernel_center[2] :
]
kernel_buffer[:, -self.kernel_center[0] :, -self.kernel_center[1] :, -self.kernel_center[2] :] = kernel[
: self.kernel_center[0], : self.kernel_center[1], : self.kernel_center[2]
]
self.kernel = cl.array.to_device(self.queue, kernel_buffer)
# fourier transform, pointwise multiply, then invert => convolution
self.plan.execute(self.inarray.data, batch=self.batch_size)
self.plan.execute(self.kernel.data, batch=self.batch_size)
self.result_buffer = self.inarray * self.kernel
self.plan.execute(self.result_buffer.data, inverse=True, batch=self.batch_size)
result = self.result_buffer.get().astype(float)
if type == "same":
return result[
:,
self.padding_locations[0] : self.padding_locations[1],
self.padding_locations[2] : self.padding_locations[3],
self.padding_locations[4] : self.padding_locations[5],
]
elif type == "full":
return result[
:,
self.full_locations[0] : self.full_locations[1],
self.full_locations[2] : self.full_locations[3],
self.full_locations[4] : self.full_locations[5],
]
elif type == "valid":
return result[
:,
self.valid_locations[0] : self.valid_locations[1],
self.valid_locations[2] : self.valid_locations[3],
self.valid_locations[4] : self.valid_locations[5],
]
class ComputationalContext:
def __init__(self, field):
self.ocl = OpenCLSettings.Instance()
self.dispIndex = DispersionIndex.Instance()
self.grid = Grid.Instance()
self.physConst = PhysicalEnvironment.Instance()
self.ocl = OpenCLSettings.Instance()
self.errors = ComputationalErrors()
self.need_update_energy = False
self.energy = numpy.empty(1).astype(numpy.float64)
self.layer = numpy.int32(0)
self.Z = numpy.int32(0)
self.z_limit = Settings.z
self.dz = Settings.dz
self.current_dz = numpy.float64()
self.calculated_dz = Settings.dz
if Settings.z_strategy == "Uniform":
self.z_step_strategy = UniformZStepStrategy()
else:
self.z_step_strategy = AdaptiveZStepStrategy()
self.global_iteration_number = numpy.int32(0)
self.unlinear_iterations = numpy.zeros(self.grid.space_size, dtype=numpy.int32)
self.E_next = numpy.zeros((self.grid.space_size, self.grid.time_size), dtype=numpy.float64)
self.D = numpy.zeros(self.grid.time_size, dtype=numpy.float64)
self.K = numpy.zeros(self.grid.time_size, dtype=numpy.float64)
self.field_shape = field.shape
self.test_field = numpy.zeros((self.grid.space_size, self.grid.time_size), dtype=numpy.complex128)
self.mf = cl.mem_flags
self.A1_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.grid.space_grid.nbytes)
self.A2_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.grid.space_grid.nbytes)
self.A3_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.grid.space_grid.nbytes)
self.D_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.D.nbytes)
self.K_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.K.nbytes)
self.space_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=self.grid.space_grid)
self.space_delta_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=self.grid.space_delta)
self.plan1D = Plan(self.grid.time_size, dtype=numpy.complex128, queue=self.ocl.queue)
self.field_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE | self.mf.COPY_HOST_PTR, hostbuf=field)
self.unlinear_iterations_buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, self.unlinear_iterations.nbytes)
self.local_buf = cl.LocalMemory(8*self.ocl.n_threads)
self.buffers, self.sizes = self.__compute_partial_buffers(self.grid.space_size * self.grid.time_size,
self.ocl.n_threads)
self.nlnr_computing_time = numpy.float64(0)
self.diff_computing_time = numpy.float64(0)
self.disp_computing_time = numpy.float64(0)
self.copy_computing_time = numpy.float64(0)
self.energy_computing_time = numpy.float64(0)
def compute_K(self, K):
ng = self.dispIndex.n(self.physConst.w) + self.dispIndex.dng(self.physConst.w)
for i in range(self.grid.time_size):
K[i] = self.grid.freq_grid[i] * \
(self.dispIndex.n(numpy.abs(self.grid.freq_grid[i] * self.physConst.w)) - ng) / self.physConst.dnL
def compute_D(self, D):
for i in range(self.grid.time_size):
if numpy.abs(self.grid.freq_grid[i]) < 10e-8:
D[i] = 0
else:
D[i] = self.physConst.D / self.grid.freq_grid[i]
def fill_data(self):
self.compute_D(self.D)
self.compute_K(self.K)
self.ocl.initial_prg.ComputeA(self.ocl.queue, self.grid.space_grid.shape, None,
self.A1_buf, self.A2_buf, self.A3_buf, self.space_buf, self.space_delta_buf,
self.grid.space_size)
cl.enqueue_copy(self.ocl.queue, self.D_buf, self.D)
cl.enqueue_copy(self.ocl.queue, self.K_buf, self.K)
def is_stop(self):
return self.Z >= self.z_limit
def update_z(self):
self.Z += self.current_dz
self.layer += 1
def update_dz(self):
if self.calculated_dz < 0:
self.calculated_dz = Settings.dz
dz = self.calculated_dz
if self.z_limit < self.Z + dz:
dz = self.z_limit - self.Z
self.current_dz = dz
if self.current_dz < 0 and numpy.abs(self.current_dz) < 1e-10:
self.current_dz = 0
def update_energy(self):
if self.need_update_energy:
self.compute_energy()
self.need_update_energy = False
def compute_energy(self):
evt = self.ocl.error_prg.CalculateEnergy(self.ocl.queue, (self.sizes[0],), (self.ocl.n_threads,),
self.field_buf, self.space_buf, self.space_delta_buf,
self.buffers[0], self.local_buf, self.grid.time_size, self.grid.space_size)
evt.wait()
self.energy_computing_time += evt.profile.end - evt.profile.start
for i in range(1, len(self.buffers)):
evt = self.ocl.error_prg.Reduce(self.ocl.queue, (self.sizes[i],), (min(self.ocl.n_threads, self.sizes[i]),),
self.buffers[i-1], self.buffers[i], self.local_buf)
evt.wait()
self.energy_computing_time += evt.profile.end - evt.profile.start
evt = cl.enqueue_copy(self.ocl.queue, self.energy, self.buffers[-1])
evt.wait()
self.copy_computing_time += evt.profile.end - evt.profile.start
def do_step(self, dz):
iteration_number = 0
while True:
self.need_update_energy = True
if dz < 0:
self.calculated_dz = self.z_step_strategy.calculate_dz(self.calculated_dz, self.global_iteration_number,
self.errors.get_error())
self.update_dz()
else:
self.current_dz = dz
self.linear()
self.nonlinear()
if iteration_number > 50:
break
iteration_number += 1
if not (dz < 0 and self.z_step_strategy.need_update_dz(self.global_iteration_number,
self.errors.get_error())):
break
self.update_z()
def linear(self):
self.errors.linear_error.begin(self)
# Обратное преобразование Фурье
cl.enqueue_copy(self.ocl.queue, self.test_field, self.field_buf)
self.plan1D.execute(self.field_buf, batch=self.grid.space_size, inverse=True)
cl.enqueue_copy(self.ocl.queue, self.test_field, self.field_buf)
if Settings.use_difraction:
# Применяем оператор дифракции
dif_evt = self.ocl.linear_prg.Diff(self.ocl.queue, (self.grid.time_size,), None,
self.field_buf, self.A1_buf, self.A2_buf, self.A3_buf,
self.space_buf, self.space_delta_buf, self.D_buf,
self.grid.space_size, self.grid.time_size, self.current_dz)
dif_evt.wait()
self.diff_computing_time += dif_evt.profile.end - dif_evt.profile.start
cl.enqueue_copy(self.ocl.queue, self.test_field, self.field_buf)
# Применяем оператор дисперсии
disp_evt = self.ocl.linear_prg.Disp(self.ocl.queue, self.field_shape, None,
self.K_buf, self.field_buf, self.current_dz, self.grid.space_size,
self.grid.time_size)
disp_evt.wait()
cl.enqueue_copy(self.ocl.queue, self.test_field, self.field_buf)
self.disp_computing_time += disp_evt.profile.end - disp_evt.profile.start
# Прямое преобразование Фурье
self.plan1D.execute(self.field_buf, batch=self.grid.space_size, inverse=False)
self.need_update_energy = True
self.errors.linear_error.end(self)
def nonlinear(self):
if Settings.use_cubic:
self.errors.nonlinear_error.begin(self)
dt = self.grid.time_delta[1]
k = numpy.float64(self.physConst.G * self.current_dz / dt / 24.0)
max_error = numpy.float64(1e-6)
iteration = numpy.int32(0)
cl.enqueue_copy(self.ocl.queue, self.test_field, self.field_buf)
evt = self.ocl.nonlinear_prg.CubicUnlinean1DSolve(self.ocl.queue, (self.grid.space_size, 1), None,
self.field_buf, self.unlinear_iterations_buf,
k, dt, max_error, iteration, self.grid.time_size)
evt.wait()
self.nlnr_computing_time += evt.profile.end - evt.profile.start
cl.enqueue_copy(self.ocl.queue, self.test_field, self.field_buf)
cl.enqueue_copy(self.ocl.queue, self.unlinear_iterations, self.unlinear_iterations_buf)
self.global_iteration_number = numpy.ndarray.max(self.unlinear_iterations)
self.need_update_energy = True
self.errors.nonlinear_error.end(self)
def copy_from_buffer(self, field):
evt = cl.enqueue_copy(self.ocl.queue, field, self.field_buf)
evt.wait()
self.copy_computing_time += evt.profile.end - evt.profile.start
def __compute_partial_buffers(self, size, n_threads):
partial_sums = list()
sizes = list()
break_flag = True
sizes.append(size)
while break_flag:
if size / n_threads == 0:
break_flag = False
size = 1
else:
size = size / n_threads
buf_np = numpy.empty(size, dtype=numpy.float64)
buf = cl.Buffer(self.ocl.ctx, self.mf.READ_WRITE, size=buf_np.nbytes)
partial_sums.append(buf)
sizes.append(size)
return partial_sums, sizes
def gs_gpu(idata,itera=100):
"""Gerchberg-Saxton algorithm to calculate DOEs using the GPU
Calculates the phase distribution in a object plane to obtain an
specific amplitude distribution in the target plane. It uses a
FFT to calculate the field propagation.
The wavefront at the DOE plane is assumed as a plane wave.
**ARGUMENTS:**
========== ======================================================
idata numpy array containing the target amplitude distribution
itera Maximum number of iterations
========== ======================================================
"""
pl=cl.get_platforms()[0]
devices=pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,dtype=complex128) #no funciona con "complex128"
src = str(Template(KERNEL).render(
double_support=all(
has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)
))
prg = cl.Program(ctx,src).build()
idata_gpu=cl_array.to_device(queue, ifftshift(idata).astype("complex128"))
fdata_gpu=cl_array.empty_like(idata_gpu)
rdata_gpu=cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data,fdata_gpu.data)
e=1000
ea=1000
for i in range (itera):
prg.norm(queue, fdata_gpu.shape, None,fdata_gpu.data)
plan.execute(fdata_gpu.data,rdata_gpu.data,inverse=True)
tr=rdata_gpu.get()
rdata=ifftshift(tr)
#TODO: This calculation should be done in the GPU
e= (abs(rdata)-idata).std()
if e>ea:
break
ea=e
prg.norm2(queue, rdata_gpu.shape,None,rdata_gpu.data,idata_gpu.data)
plan.execute(rdata_gpu.data,fdata_gpu.data)
fdata=fdata_gpu.get()
#~ prg.norm(queue, fdata_gpu.shape, None,fdata_gpu.data)
fdata=ifftshift(fdata)
fdata=exp(1.j*angle(fdata))
#~ fdata=fdata_gpu.get()
return fdata
freq_hz = freq * 1000000.0 blocklen = (32 * 1024) # bring up OpenCL from pyfft.cl import Plan import numpy import pyopencl as cl import pyopencl.array as clarray import pyopencl.clmath as clmath ctx = cl.create_some_context(interactive=False) queue = cl.CommandQueue(ctx) plan = Plan(blocklen, queue=queue, dtype=numpy.complex64) lowpass_filter_b, lowpass_filter_a = sps.butter(8, (4.5/(freq/2)), 'low') # stubs for later f_deemp_b = [] f_deemp_a = [] # default deemp constants deemp_t1 = .825 deemp_t2 = 2.35 # audio filters Baudiorf = sps.firwin(65, 3.5 / (freq / 2), window='hamming', pass_zero=True) afreq = freq / 4
from pyfft.cl import Plan import numpy import pyopencl as cl import pyopencl.array as cl_array # initialize context ctx = cl.create_some_context() queue = cl.CommandQueue(ctx) # create plan plan = Plan((16, 16), queue=queue) # prepare data data = numpy.ones((16, 16), dtype=numpy.complex64) gpu_data = cl_array.to_device(ctx, queue, data) print gpu_data # forward transform plan.execute(gpu_data.data) result = gpu_data.get() print result # inverse transform plan.execute(gpu_data.data, inverse=True) result = gpu_data.get() error = numpy.abs(numpy.sum(numpy.abs(data) - numpy.abs(result)) / data.size) print error < 1e-6
def myfunc(d_g):
from pyfft.cl import Plan
from gputools import get_device
plan = Plan(d_g.shape, queue = get_device().queue, fast_math = True)
plan.execute(d_g.data,d_g.data)
plan.execute(d_g.data,d_g.data, inverse = True)
def test_algorithmC(self, ofdm_mode, guardinterval):
print "\n**************************"
print "test ofdm opencl ifft w/o fftshift"
passed = 0
linecnt = 1
g = 0
size = 0
# create a fft plan
self.fftplan = Plan(ofdm_mode,dtype=numpy.complex128,normalize=True, fast_math=True,context=self.ctx, queue=self.queue)
# opencl buffer holding data for the ifft - including pilots # 8k or 2k size ???
fftbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=ofdm_mode*16)
#size of guiardinterval destination buffer
dest_buf_size = ofdm_mode*(1+guardinterval)
# opencl buffer holding the computed data
dest_buf = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=int(dest_buf_size*16) )
encoded_data = numpy.array(numpy.zeros(dest_buf_size), dtype=numpy.complex128)
if ofdm_mode == 8192:
size = 6817
print "8k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_8K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_8K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_8K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_8K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_8K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
elif ofdm_mode == 2048:
size = 1705
print "2k mode"
if guardinterval == 0.25:
self.fd_input = open('test_bench_ofdm_input_2K_1_4.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_4.csv', 'r')
print "1/4 guard interval"
g = guardinterval
if guardinterval == 0.125:
self.fd_input = open('test_bench_ofdm_input_2K_1_8.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_8.csv', 'r')
print "1/8 guard interval"
g = guardinterval
if guardinterval == 0.0625:
self.fd_input = open('test_bench_ofdm_input_2K_1_16.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_16.csv', 'r')
print "1/16 guard interval"
g = guardinterval
if guardinterval == 0.03125:
self.fd_input = open('test_bench_ofdm_input_2K_1_32.csv', 'r')
self.fd_output = open('test_bench_ofdm_output_2K_1_32.csv', 'r')
print "1/32 guard interval"
g = guardinterval
if g == 0:
print "wrong guardinterval specified"
return
if size == 0:
print "wrong ofdm_mode"
return
for line in self.fd_input:
data_to_encode = [float(0) + 1j*float(0)] * ofdm_mode
#counter = (2048-size-1)/2+1
counter = 0
for tmp in line.split(","):
if string.find(tmp, " + ") > -1:
data_to_encode[counter]=(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
data_to_encode[counter]=(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
counter += 1
data_to_encode = numpy.array(data_to_encode, dtype=numpy.complex128)
reference_data = []
for tmp in self.fd_output.readline().split(","):
if string.find(tmp, " + ") > -1:
reference_data.append(float(tmp.split(" + ")[0]) + 1j * float(string.replace(tmp.split(" + ")[1],"i","")))
if string.find(tmp, " - ") > -1:
reference_data.append(float(tmp.split(" - ")[0]) - 1j * float(string.replace(tmp.split(" - ")[1],"i","")))
reference_data = numpy.array(reference_data, dtype=numpy.complex128)
# do fftshift
tmp = [float(0) + 1j*float(0)] * ofdm_mode
for i in range(0,size):
tmp[(ofdm_mode-size+1)/2+i] = data_to_encode[i]
for i in range(0,ofdm_mode/2):
data_to_encode[i] = tmp[ofdm_mode/2+i]
data_to_encode[i+ofdm_mode/2] = tmp[i]
cl.enqueue_copy(self.queue, fftbuffer, data_to_encode).wait()
self.fftplan.execute(fftbuffer, data_out=None, inverse=True, batch=1, wait_for_finish=True)
# add guard interval
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(16*ofdm_mode), src_offset=0, dest_offset=int(ofdm_mode*guardinterval*16)).wait()
cl.enqueue_copy(self.queue, dest_buf, fftbuffer, byte_count=int(ofdm_mode*guardinterval*16), src_offset=int(ofdm_mode - ofdm_mode*guardinterval)*16 ,dest_offset=0).wait()
cl.enqueue_copy(self.queue, encoded_data, dest_buf).wait()
if numpy.allclose(reference_data, encoded_data, rtol=1.0000000000000001e-04, atol=1e-06):
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
#print data_to_encode
print "encoded data[0]:"
print encoded_data[0]
print "reference data[0]:"
print reference_data[0]
print "error data:"
#print reference_data - encoded_data
linecnt += 1
print "%d pass out of %d" % (passed, linecnt-1)
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All ofdm ifft tests PASS\n"
return True
else:
print "at least one ofdm ifft test FAILED\n"
return False
def gs_gpu(idata, itera=100):
"""Gerchberg-Saxton algorithm to calculate DOEs using the GPU
Calculates the phase distribution in a object plane to obtain an
specific amplitude distribution in the target plane. It uses a
FFT to calculate the field propagation.
The wavefront at the DOE plane is assumed as a plane wave.
**ARGUMENTS:**
========== ======================================================
idata numpy array containing the target amplitude distribution
itera Maximum number of iterations
========== ======================================================
"""
pl = cl.get_platforms()[0]
devices = pl.get_devices(device_type=cl.device_type.GPU)
ctx = cl.Context(devices=[devices[0]])
queue = cl.CommandQueue(ctx)
plan = Plan(idata.shape, queue=queue,
dtype=complex128) #no funciona con "complex128"
src = str(
Template(KERNEL).render(
double_support=all(has_double_support(dev) for dev in devices),
amd_double_support=all(
has_amd_double_support(dev) for dev in devices)))
prg = cl.Program(ctx, src).build()
idata_gpu = cl_array.to_device(queue,
ifftshift(idata).astype("complex128"))
fdata_gpu = cl_array.empty_like(idata_gpu)
rdata_gpu = cl_array.empty_like(idata_gpu)
plan.execute(idata_gpu.data, fdata_gpu.data)
e = 1000
ea = 1000
for i in range(itera):
prg.norm(queue, fdata_gpu.shape, None, fdata_gpu.data)
plan.execute(fdata_gpu.data, rdata_gpu.data, inverse=True)
tr = rdata_gpu.get()
rdata = ifftshift(tr)
#TODO: This calculation should be done in the GPU
e = (abs(rdata) - idata).std()
if e > ea:
break
ea = e
prg.norm2(queue, rdata_gpu.shape, None, rdata_gpu.data, idata_gpu.data)
plan.execute(rdata_gpu.data, fdata_gpu.data)
fdata = fdata_gpu.get()
#~ prg.norm(queue, fdata_gpu.shape, None,fdata_gpu.data)
fdata = ifftshift(fdata)
fdata = exp(1.j * angle(fdata))
#~ fdata=fdata_gpu.get()
return fdata
def myfunc(d_g):
from pyfft.cl import Plan
from gputools import get_device
plan = Plan(d_g.shape, queue=get_device().queue, fast_math=True)
plan.execute(d_g.data, d_g.data)
plan.execute(d_g.data, d_g.data, inverse=True)
class SplitStep(object):
'''
An OpenCL version of the split-step parabolic equation solver.
'''
_kernel = util.srcpath(__file__, 'clsrc', 'splitstep.mako')
def __init__(self, k0, nx, ny, h, d=None, l=10, dz=None,
w=0.39, propcorr=None, phasetol=None, context=None):
'''
Initialize a split-step engine over an nx-by-ny grid with
isotropic step size h. The unitless wave number is k0. The wave
is advanced in steps of dz or (if dz is not provided) h.
If d is provided, it is a 4-tuple that describes the
directivity of any source as d = (dx, dy, dz, w), where (dx,
dy, dz) is the directivity axis and w is the beam width
parameter. Otherwise, all sources are treated as point sources.
If l is specified and greater than zero, it is the width of a
Hann window used to attenuate the field along each edge.
The parameter w (as a multiplier 1 / w**2) governs the
high-order spectral cross term.
If propcorr is specified, it should be a tuple of Booleans of
the form (hospec, hospat) that determines whether corrections
involving high-order spectral or spatial terms, respectively,
are used in the propagator by default. If propcorr is
unspecified, both corrections are used.
The parameter phasetol specifies the maximum permissible phase
deviation (in fractions of pi), relative to propagation through
the homogeneous background, incurred by propagating through the
inhomogeneous medium. The number of steps per slab will be
adjusted so that the phase shift incurred by propagating
through materials with the most extreme sound speeds will never
exceed phasetol.
'''
# Ensure that the phase tolerance is not too small
# Otherwise, number of propagation steps will blow up uncontrollably
if phasetol is not None and abs(phasetol) < 1e-6:
raise ValueError('Phase tolerance must be greater than 1e-6')
# Copy the parameters
self.grid = nx, ny
self.h, self.k0, self.l = h, k0, l
self.w = np.float32(1. / w**2)
self.phasetol = phasetol
# Specify the use of corrective terms
if propcorr is not None:
self.propcorr = tuple(propcorr)
else: self.propcorr = (True, True)
# Set the step length
self.dz = dz if dz else h
# Grab the provided context or create a default
self.context = util.grabcontext(context)
# Build the program for the context
t = Template(filename=self._kernel, output_encoding='ascii')
src = t.render(grid = self.grid, k0=k0, h=h, d=d, l=l)
self.prog = cl.Program(self.context, src).build()
# Create a command queue for forward propagation calculations
self.fwdque = cl.CommandQueue(self.context)
# Create command queues for transfers
self.recvque = cl.CommandQueue(self.context)
self.sendque = cl.CommandQueue(self.context)
# Create an FFT plan in the OpenCL propagation queue
# Reorder the axes to conform with row-major ordering
self.fftplan = Plan((ny, nx), queue=self.fwdque)
grid = self.grid
def newbuffer():
nbytes = cutil.prod(grid) * np.complex64().nbytes
flags = cl.mem_flags.READ_WRITE
return util.SyncBuffer(self.context, flags, size=nbytes)
# Buffers to store the propagating (twice) and backward fields
self.fld = [newbuffer() for i in range(3)]
# Scratch space used during computations
self.scratch = [newbuffer() for i in range(3)]
# The index of refraction gets two buffers for transmission
self.obj = [newbuffer() for i in range(2)]
# Two buffers are used for the Goertzel FFT of the contrast source
self.goertzbuf = [newbuffer() for i in range(2)]
# The sound speed extrema for the current slab are stored here
self.speedlim = [1., 1.]
# Initialize buffer to hold results of advance()
self.result = newbuffer()
# By default, volume fields will be transfered from the device
self._goertzel = False
# Initialize refractive index and fields
self.reset()
# By default, device exchange happens on the full grid
self.rectxfer = util.RectangularTransfer(grid, grid, np.complex64, alloc_host=False)
def slicecoords(self):
'''
Return the meshgrid coordinate arrays for an x-y slab in the
current engine. The origin is in the center of the slab.
'''
g = self.grid
cg = np.ogrid[:g[0], :g[1]]
return [(c - 0.5 * (n - 1.)) * self.h for c, n in zip(cg, g)]
def reset(self, propcorr = None, goertzel = False):
'''
Reset the propagating and backward fields to zero and the prior
refractive index buffer to unity.
If propcorr is provided, it should be tuple as described in the
docstring for __init__(). This will change the default behavior
of corrective terms in the propagator.
If goertzel is True, calls to advance() with shift=True will
not copy the shifted, combined field from the device. Instead,
the field will be used in a Goertzel algorithm to compute
(slice by slice) the Fourier transform, restricted to the unit
sphere, of induced scattering sources.
'''
if propcorr is not None: self.propcorr = tuple(propcorr)
grid = self.grid
z = np.zeros(grid, dtype=np.complex64)
for a in self.fld + self.obj + self.goertzbuf:
cl.enqueue_copy(self.fwdque, a, z, is_blocking=False)
self.speedlim = [1., 1.]
self._goertzel = goertzel
def setroi(self, rgrid):
'''
Set a region of interest that will limit device transfers
within the computational grid.
'''
self.rectxfer = util.RectangularTransfer(self.grid, rgrid, np.complex64, alloc_host=False)
def setincident(self, srcloc, idx = 0):
'''
Set the value of the CL field buffer at index idx to the
incident field at a location srcloc represented as a 3-tuple.
The field plane is always assumed to have a z-height of 0; the
z coordinate of srcloc is therefore the height of the source
above the field plane. The transverse origin (x, y) = (0, 0)
corresponds to the midpoint of the field plane.
'''
inc = self.fld[idx]
sx, sy, dz = [np.float32(s) for s in srcloc]
self.prog.green3d(self.fwdque, self.grid, None, inc, sx, sy, dz)
def objupdate(self, obj):
'''
Update the rolling buffer with the index of refraction,
corresponding to an object contrast obj, for the next slab.
The transmission queue is used for updates to facilitate
concurrency with forward propagation algorithms.
'''
# Transfers occur in the transmission queue for concurrency
prog, queue, grid = self.prog, self.recvque, self.grid
# Roll the buffer so the next slab is second
nxt, cur = self.obj
self.obj = [cur, nxt]
# Figure approximate sound speed extrema in the upcoming slab
# If the imaginary part of the wave number is negligible
# compared to the real, maximum sound speed corresponds to the
# minimum contrast and vice versa
if self.phasetol:
ctextrema = [np.max(obj.real), np.min(obj.real)]
self.speedlim = [1 / np.sqrt(ctv + 1.) for ctv in ctextrema]
# Ensure buffer is not used by prior calculations
nxt.sync(queue)
# Transfer the object contrast into the next-slab buffer
evt = self.rectxfer.todevice(queue, nxt, obj)
# Return buffers of the current and next slabs and a transfer event
return cur, nxt, evt
def propagate(self, fld = None, dz = None, idx = 0, corr = None):
'''
Propagate the field stored in the device buffer fld (or, if fld
is None, the current in-device field at index idx) a step dz
(or, if dz is None, the default step size) through the
currently represented medium.
If corr is not None, it should be a tuple as described in the
reset() docstring to control the use of corrective terms in the
spectral propagator. Otherwise, the instance default is used.
'''
prog, grid = self.prog, self.grid
fwdque = self.fwdque
hospec, hospat = corr if corr is not None else self.propcorr
# Point to the field, scratch buffers, and refractive index
if fld is None: fld = self.fld[idx]
u, v, x = self.scratch
obj = self.obj[0]
# These constants will be used in field computations
one = np.float32(1.)
dz = np.float32(dz if dz is not None else self.dz)
# Attenuate the boundaries using a Hann window, if desired
if self.l > 0:
prog.attenx(fwdque, (self.l, grid[1]), None, fld)
prog.atteny(fwdque, (grid[0], self.l), None, fld)
# Multiply, in v, the field by the contrast
if hospec: prog.ctmul(fwdque, grid, None, v, obj, fld)
# Multiply, in u, the field by the high-order spatial operator
if hospat: prog.hospat(fwdque, grid, None, u, obj, fld)
# From here, the field should be spectral
self.fftplan.execute(fld)
# Compute high-order spatial corrections or set the buffer to NULL
if hospat:
# With high-order spatial terms, transform u as well
self.fftplan.execute(u)
# Compute the scaled, spectral Laplacians of u and the field (in x)
prog.laplacian(fwdque, grid, None, u, u)
prog.laplacian(fwdque, grid, None, x, fld)
# Apply the high-order spatial operator to x
self.fftplan.execute(x, inverse=True)
prog.hospat(fwdque, grid, None, x, obj, x)
self.fftplan.execute(x)
# Add x to u to get the high-order spatial corrections
prog.caxpy(fwdque, grid, None, x, one, u, x)
else: x = None
# Compute high-order spectral corrections or set buffers to NULL
if hospec:
# Apply, in u, the high-order spectral operator to the field
prog.hospec(fwdque, grid, None, u, fld)
# Multiply u by the contrast in the spatial domain
self.fftplan.execute(u, inverse=True)
prog.ctmul(fwdque, grid, None, u, obj, u)
# Let v = v + u / w**2 in the spatial domain
prog.caxpy(fwdque, grid, None, v, self.w, u, v)
# Transform u and v into the spectral domain
self.fftplan.execute(u)
self.fftplan.execute(v)
# Apply the high-order spectral operator to the new v
prog.hospec(fwdque, grid, None, v, v)
else: u, v = None, None
# Add all appropriate high-order corrections to the field
if hospat or hospec:
prog.corrfld(fwdque, grid, None, fld, u, v, x, dz)
# Propagate the field through a homogeneous slab
prog.propagate(fwdque, grid, None, fld, dz)
# Take the inverse FFT of the field and the Laplacian
self.fftplan.execute(fld, inverse=True)
# Multiply by the phase screen, returning the event
return prog.screen(fwdque, grid, None, fld, obj, dz)
def advance(self, obj, shift=False, corr=None, shcorr=None):
'''
Propagate a field through the current slab and transmit it
through an interface with the next slab characterized by object
contrast obj. The transmission overwrites the refractive index
of the current slab with the interface reflection coefficients.
If shift is True, the forward is shifted by half a slab to
agree with full-wave solutions and includes a
backward-traveling contribution caused by reflection from the
interface with the next slab.
The relevant result (either the forward field or the
half-shifted combined field) is copied into a device-side
buffer for later retrieval and handling.
If corr is not None, it should be a tuple as specified in the
reset() docstring to override the default use of corrective
terms in the spectral propagator.
The argument shcorr is interpreted exactly as corr, but is used
instead of corr for the propagation used to shift the field to
the center of the slab.
'''
prog, grid = self.prog, self.grid
fwdque, recvque, sendque = self.fwdque, self.recvque, self.sendque
# Point to the field components
fwd, bck, buf = [f for f in self.fld]
if shift:
# Ensure that a prior copy isn't using the buffer
buf.sync(fwdque)
# Copy the forward field for shifting if necessary
cl.enqueue_copy(fwdque, buf, fwd)
# Copy the sound speed extrema for the current slab
speedlim = list(self.speedlim)
# Push the next slab to its buffer (overwrites speed extrema)
ocur, onxt, obevt = self.objupdate(obj)
if self.phasetol is not None:
# Figure maximum propagation distance to not
# exceed maximum permissible phase deviation
dzl = []
for spd in speedlim:
# Sign governs the sign of the phase deviation,
# which is irrelevant, so ignore it here
spdiff = max(abs(spd - 1.), 1e-8)
# Preventing spdiff from reaching zero limits
# maximum permissible propagation distance
dzl.append(abs(0.5 * self.phasetol * spd / spdiff))
# Subdivide the slab into maximum propagation distance
nsteps = max(1, int(np.round(self.dz / min(dzl))))
else: nsteps = 1
dz = self.dz / nsteps
# Ensure that no prior copy is using the field buffer
fwd.sync(fwdque)
# Propagate the forward field through the slab on the fwdque
for i in range(nsteps): self.propagate(fwd, dz, corr=corr)
# Ensure next slab has been received before handling interface
cl.enqueue_barrier(fwdque, wait_for=[obevt])
# Compute transmission through the interface
# The reflected field is only of interest if a shift is desired
transevt = prog.txreflect(fwdque, grid, None,
fwd, bck if shift else None, ocur, onxt)
# Hold the current contrast slab until the transmission is done
ocur.attachevent(transevt)
if shift:
# Add the forward and backward fields
prog.caxpy(fwdque, grid, None, buf, np.float32(1.), buf, bck)
# Propagate the combined field a half step
# Save the propagation event for delaying result copies
pevt = self.propagate(buf, 0.5 * self.dz, corr=shcorr)
# Handle Goertzel iterations to compute the Fourier
# transform of the contrast source on the unit sphere
if self._goertzel:
# Compute the FFT of the source in the XY plane
crt = self.scratch[0]
prog.ctmul(fwdque, grid, None, crt, ocur, buf)
self.fftplan.execute(crt)
# Compute the next Goertzel iteration
pn1, pn2 = self.goertzbuf
dz = np.float32(self.dz)
# The final argument (slab count) is not yet used
nz = np.int32(0)
prog.goertzelfft(fwdque, grid, None, pn1, pn2, crt, dz, nz)
# Cycle the Goertzel buffers
self.goertzbuf = [pn2, pn1]
else:
# Copy the shifted field into the result buffer
# No result sync necessary, all mods occur on sendque
evt = cl.enqueue_copy(sendque, self.result, buf, wait_for=[pevt])
# Attach the copy event to the source buffer
buf.attachevent(evt)
else:
# Copy the forward field into the result buffer
# Wait for transmissions to finish for consistency
evt = cl.enqueue_copy(sendque, self.result, fwd, wait_for=[transevt])
# Attach the copy event to the field buffer
fwd.attachevent(evt)
def getresult(self, hbuf):
'''
Wait for the intra-device transfer of the previous result to
the result buffer, and initiate a device-to-host copy of the
valid result buffer into hbuf.
An event corresponding to the transfer is returned.
'''
sendque = self.sendque
# Initiate the rectangular transfer on the transfer queue
# No sync necessary, all mods to result buffer occur on sendque
evt = self.rectxfer.fromdevice(sendque, self.result, hbuf)[1]
# Attach the copy event to the result buffer
self.result.attachevent(evt)
return evt
def goertzelfft(self, nz = 0):
'''
Finish Goertzel iterations carried out in repeated calls to
advance() and copy the positive and negative hemispheres of the
Fourier transform of the contrast source to successive planes
of a Numpy array.
If nz is specified, it is the number of slabs involved in the
Fourier transform and is used to properly scale the output of
the Goertzel algorithm. When nz = 0, no scaling is performed.
Copies are synchronous and are done on the forward propagation
queue.
'''
prog, grid = self.prog, self.grid
fwdque = self.fwdque
hemispheres = np.zeros(list(grid) + [2], dtype=np.complex64, order='F')
# If the spectral field hasn't been computed, just return zeros
if not self._goertzel: return hemispheres
# Finalize the Goertzel iteration
pn1, pn2 = self.goertzbuf
dz = np.float32(self.dz)
nz = np.int32(nz)
# Pass None as the contrast current to signal final iteration
# After this, pn1 is the positive hemisphere, pn2 is the negative
prog.goertzelfft(fwdque, grid, None, pn1, pn2, None, dz, nz)
# Copy the two hemispheres into planes of an array
cl.enqueue_copy(fwdque, hemispheres[:,:,0:1], pn1, is_blocking=False)
cl.enqueue_copy(fwdque, hemispheres[:,:,1:2], pn2, is_blocking=True)
return hemispheres
def __init__(self, k0, nx, ny, h, d=None, l=10, dz=None,
w=0.39, propcorr=None, phasetol=None, context=None):
'''
Initialize a split-step engine over an nx-by-ny grid with
isotropic step size h. The unitless wave number is k0. The wave
is advanced in steps of dz or (if dz is not provided) h.
If d is provided, it is a 4-tuple that describes the
directivity of any source as d = (dx, dy, dz, w), where (dx,
dy, dz) is the directivity axis and w is the beam width
parameter. Otherwise, all sources are treated as point sources.
If l is specified and greater than zero, it is the width of a
Hann window used to attenuate the field along each edge.
The parameter w (as a multiplier 1 / w**2) governs the
high-order spectral cross term.
If propcorr is specified, it should be a tuple of Booleans of
the form (hospec, hospat) that determines whether corrections
involving high-order spectral or spatial terms, respectively,
are used in the propagator by default. If propcorr is
unspecified, both corrections are used.
The parameter phasetol specifies the maximum permissible phase
deviation (in fractions of pi), relative to propagation through
the homogeneous background, incurred by propagating through the
inhomogeneous medium. The number of steps per slab will be
adjusted so that the phase shift incurred by propagating
through materials with the most extreme sound speeds will never
exceed phasetol.
'''
# Ensure that the phase tolerance is not too small
# Otherwise, number of propagation steps will blow up uncontrollably
if phasetol is not None and abs(phasetol) < 1e-6:
raise ValueError('Phase tolerance must be greater than 1e-6')
# Copy the parameters
self.grid = nx, ny
self.h, self.k0, self.l = h, k0, l
self.w = np.float32(1. / w**2)
self.phasetol = phasetol
# Specify the use of corrective terms
if propcorr is not None:
self.propcorr = tuple(propcorr)
else: self.propcorr = (True, True)
# Set the step length
self.dz = dz if dz else h
# Grab the provided context or create a default
self.context = util.grabcontext(context)
# Build the program for the context
t = Template(filename=self._kernel, output_encoding='ascii')
src = t.render(grid = self.grid, k0=k0, h=h, d=d, l=l)
self.prog = cl.Program(self.context, src).build()
# Create a command queue for forward propagation calculations
self.fwdque = cl.CommandQueue(self.context)
# Create command queues for transfers
self.recvque = cl.CommandQueue(self.context)
self.sendque = cl.CommandQueue(self.context)
# Create an FFT plan in the OpenCL propagation queue
# Reorder the axes to conform with row-major ordering
self.fftplan = Plan((ny, nx), queue=self.fwdque)
grid = self.grid
def newbuffer():
nbytes = cutil.prod(grid) * np.complex64().nbytes
flags = cl.mem_flags.READ_WRITE
return util.SyncBuffer(self.context, flags, size=nbytes)
# Buffers to store the propagating (twice) and backward fields
self.fld = [newbuffer() for i in range(3)]
# Scratch space used during computations
self.scratch = [newbuffer() for i in range(3)]
# The index of refraction gets two buffers for transmission
self.obj = [newbuffer() for i in range(2)]
# Two buffers are used for the Goertzel FFT of the contrast source
self.goertzbuf = [newbuffer() for i in range(2)]
# The sound speed extrema for the current slab are stored here
self.speedlim = [1., 1.]
# Initialize buffer to hold results of advance()
self.result = newbuffer()
# By default, volume fields will be transfered from the device
self._goertzel = False
# Initialize refractive index and fields
self.reset()
# By default, device exchange happens on the full grid
self.rectxfer = util.RectangularTransfer(grid, grid, np.complex64, alloc_host=False)
def fft_plan(shape, **kwargs):
"""returns an opencl/pyfft plan of shape dshape
kwargs are the same as pyfft.cl.Plan
"""
return Plan(shape, queue=get_device().queue, **kwargs)
